©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved

Fascinating Tales of a Strange Tomorrow Julien Simon"Principal Technical Evangelist julsimon@amazon.fr @julsimon

John McCarthy Coined the term “Artificial Intelligence” "

Invented LISP (1958)"Received Turing Award (1971)

Forbidden Planet

1956 Dartmouth Summer Research Project

Robbie the Robot

Predictions

•  1958 H. A. Simon and Allen Newell: “within 10 years a digital computer will be the world's chess champion” and “within 10 years a digital computer will discover and prove an important new mathematical theorem”

•  1965 H. A. Simon: “machines will be capable, within 20 years, of doing any work a man can do”

•  1967 Marvin Minsky: “Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved.”

•  1970 Marvin Minsky: “In from 3 to 8 years we will have a machine with the general intelligence of an average human being”

https://en.wikipedia.org/wiki/History_of_artificial_intelligence

“It’s 2001. Where is HAL?”

Marvin Minsky Co-founded the MIT AI lab (1959)

Advised Kubrick on 2001: A Space Odyssey (1968) Received Turing Award (1969)

HAL 9000 HAL Laboratories (1992)

Meanwhile, on the US West Coast…

Millions of users… Mountains of data… Commodity hardware…"Bright engineers… Need to make money….

Gasoline waiting for a match!

The Machine Learning explosion

•  12/2004 - Google publishes Map Reduce paper

•  04/2006 - Hadoop 0.1

•  05/2009 – Yahoo sorts a Terabyte in 62 seconds

•  The rest is history

Fast forward a few years

•  ML is now a commodity

•  Great, but still no HAL in sight •  Traditional Machine Learning doesn’t work well with

complex problems such as computer vision, computer speech or natural language processing

•  Another AI winter, then?

Neural networks •  Through training, a neural network self-organizes and discovers features

automatically: the more data, the better (unlike traditional ML)

•  “Universal approximation machine” (Andrew Ng) –  Artificial Intelligence is the New Electricity https://www.youtube.com/watch?v=21EiKfQYZXc

•  Not new technology!

–  Perceptron (Rosenblatt, 1958) –  Backpropagation (Werbos, 1975)

•  They failed back then because –  data sets were too small –  computing power was not available

Why it’s different this time

•  Large data sets are available –  Imagenet : 14M+ images http://www.image-net.org/

•  GPUs and FPGAs deliver unprecedented amounts of computing power.

–  It’s now possible to train networks that have hundreds of layers

•  Scalability and elasticity are key assets for Deep Learning –  Grab a lot of storage and compute resources for training, then release them –  Using a DL model is lightweight: you can do it on a Raspberry Pi!

I for one welcome our new "Deep Learning Overlords

Sorting cucumbers in Japan

https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow

Detecting plant diseases

https://blogs.nvidia.com/blog/2016/12/13/ai-fights-plant-disease/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5032846/

Improving hearing aids

http://spectrum.ieee.org/consumer-electronics/audiovideo/deep-learning-reinvents-the-hearing-aid

Flipping burgers

http://misorobotics.com/

Flippy

Amazon Echo

Now what?

Dive as deep as you need to

Hardware – EC2, GPU, FPGA

ML/DL – MXNet, TensorFlow, Caffe, Torch, Theano

Platform – EMR, Spark, Notebooks, Models

High-level services Rekognition, Polly, Lex

AWS GPU Instances

•  g2 (2xlarge, 8xlarge) –  32 vCPUs, 60 GB RAM –  4 NVIDIA K520 GPUs –  16 GB of GPU memory, 6144 CUDA cores

•  p2 (xlarge, 8xlarge, 16xlarge) –  64 vCPUs, 732 GB RAM –  16 NVIDIA GK210 GPUs –  192 GB of GPU memory, 39936 CUDA cores –  20 Gbit/s networking

https://aws.amazon.com/blogs/aws/new-g2-instance-type-with-4x-more-gpu-power/ https://aws.amazon.com/blogs/aws/new-p2-instance-type-for-amazon-ec2-up-to-16-gpus/ https://aws.amazon.com/ec2/Elastic-GPUs/

AWS Deep Learning AMI

•  Deep Learning Frameworks – 5 popular Deep Learning Frameworks (mxnet, Caffe, Tensorflow, Theano, and Torch) all prebuilt and pre-installed

•  Pre-installed components – Nvidia drivers, cuDNN, Anaconda, Python2 and Python3

•  AWS Integration – Packages and configurations that provide tight integration with Amazon Web Services like Amazon EFS (Elastic File System)

•  Amazon Linux & Ubuntu

https://aws.amazon.com/about-aws/whats-new/2017/03/deep-learning-ami-release-v2-0-now-available-for-amazon-linux/

mxnet

mxnet resources" http://mxnet.io/ "https://github.com/dmlc/mxnet "https://github.com/dmlc/mxnet-notebooks " http://www.allthingsdistributed.com/2016/11/mxnet-default-framework-deep-learning-aws.html https://github.com/awslabs/deeplearning-cfn

mxnet demo""

Deep Learning AMI on p2.16xlarge"Training and predicting the MNIST data set "

"

MNIST dataset

http://yann.lecun.com/exdb/mnist/ 70,000 handwritten digits 28x28 pixels Greyscale (0 to 255)

Multilayer perceptron

Train and test

INFO:root:Epoch[9] Batch [5600] Speed: 6475.51 samples/sec Train-accuracy=0.987500INFO:root:Epoch[9] Batch [5800] Speed: 6541.05 samples/sec Train-accuracy=0.988000INFO:root:Epoch[9] Batch [6000] Speed: 6481.38 samples/sec Train-accuracy=0.988000INFO:root:Epoch[9] Resetting Data IteratorINFO:root:Epoch[9] Time cost=9.317INFO:root:Epoch[9] Validation-accuracy=0.963600

Web visualization http://scs.ryerson.ca/~aharley/vis/fc/

Now the hard questions…

•  Can my business benefit from Deep Learning? –  DL : “solving the tasks that are easy for people to perform but hard to describe formally”

•  Should I build my own network? –  Do I have the expertise? –  Do I have enough time, data & compute to train it?

•  Or should I use a pre-trained model? –  How well does it fit my use case? –  On what data was it trained?

•  Or should I use a high-level service?

•  Same questions as ML years ago J

Science catching up with Fiction ?

May 2016: AI defeats Lee Sedol, "Go world champion

October 2014: Tesla Autopilot

October 2015: 30,000 robots in Amazon Fulfillment Centers

Will man-like machines rule the world? Who knows? Whatever happens, these will be fascinating tales of a strange tomorrow.

©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved

Julien Simon julsimon@amazon.fr @julsimon

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